This article will explain how to create four dimensional objects, in a format compatible with the Unity4D extension.
You can find all the articles in this series here:
This article will explain how to render 4D objects in Unity, using three separate technique: orthographic projection, perspective projection and cross-section.
You can find all the articles in this series here:
A link to download the Unity4D package can be found at the end of this article.
This article will show how to extend Unity to support four-dimensional geometry. This is the second article in a series of four, and the first one which will probably start discussing the Mathematics and the C# code necessary to store and manipulate 4D objects in Unity.
You can find all the articles in this series here:
A link to download the Unity4D package can be found at the end of this article.
This is the first part of a series of articles dedicated to extending Unity from 3D to 4D. In this instalment, we will explore the fourth dimension, from its representations in movies and video games, to its more mathematical and geometrical interpretations.
At the end of the series, you will learn how to create and manipulate 4D objects inside a modern game engine like Unity or Unreal.
You can find all the articles in this series here:
A link to download the Unity4D package can be found at the end of this article.
A slippy map (sometimes also referred to as tiled web map, tile map, or simply interactive map) is a web-based map that can be zoomed in and out seamlessly. The most popular slippy map you might be familiar with is Google Maps, alongside many others like that.
This tutorial will show you how to create your own web-based slippy map with Unity. Such a technique can be used to create interactive maps for your own games, or to better explore complex phenomena such as fractals or even Mathematical functions. For clarity, the actual map will be created with Unity, but it will be served using a JavaScript library called LeafletJS. A link to download the full Unity package is also available at the end of the article.
The image used in the cover has been generated using Stamen.
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Seam carving is a technique that can be used to resize images, which is also known as liquid rescaling. Compared to the traditional resizing tool, it does not “stretch” the image, but it selectively removes the pixels which contain the least amount of information. As a result, it allows to shrink images preserving most of the details.
Seam carving is an example of a context-aware resizing algorithm, as it does not treat images as mere collections of pixels. By all means, it can be considered an AI-powered algorithm. The “AI part” resides in the fact that it is able to identify which pixels to remove on its own. However, it does so without any neural network and—most importantly—without the need to be trained on external data. Hence, it belongs to the field of what I call Classical AI, conversely to the more recent field of Deep Learning. With AI-powered tools becoming more and more popular, I find it helpful to show how a lot can be achieved with some clever algorithms, without the need to train expensive neural network models.
If you are interested in learning more about tools like DALL·E 2 and Midjourney, I would suggest checking one of my most detailed articles titled The Rise of AI Art.
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Over the past ten years, Artificial Intelligence (AI) and Machine Learning (ML) have steadily crept into the Art Industry. From Deepfakes to DALL·E, the impact of these new technologies can be longer be ignored, and many communities are now on the edge of a reckoning. On one side, the potential for modern AIs to generate and edit both images and videos is opening new job opportunities for millions; but on the other is also threatening a sudden and disruptive change across many industries.
The purpose of this long article is to serve as an introduction to the complex topic of AI Art: from the technologies that are powering this revolution, to the ethical and legal issues they have unleashed. While this is still an ongoing conversation, I hope it will serve as a primer for anyone interested in better understanding these phenomena—especially journalists who are keen to learn more about the benefits, changes and challenges that that AI will inevitably bring into our own lives. And since the potential of these technologies—and the best way to use them—are still being explored, there will likely be more questions and tentative suggestions, rather than definite answers.
In this article I will try to keep a positive outlook, as I feel is important to show and inspire people on how to better harness this technology, rather than just demonising it. And while predicting the future is beyond the scope of this article, there will be plenty of examples of how new art practices and technologies have impacted art communities in the past.
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This post completes the journey started in The Mathematics of Thin-Film Interference, by explaining how to turn the equations previously presented into actual shader code.
You can find the complete series here:
A link to download the Unity project used in this series is also provided at the end of the page.
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This post continues our journey through the Mathematical foundations of iridescence. This time, we will discuss a new way in which material can split light: thin-film interference. This is how bubbles (and car paint) get their unique reflections.
You can find the complete series here:
A link to download the Unity project used in this series is also provided at the end of the page.
This is the third part of the series dedicated to one of the most popular sensor de-noising technique: Kalman filters. This article will explain how to model non-linear processes to improve the filter performance, something known as the Extended Kalman Filter.
You can read all the tutorials in this online course here:
